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Proceedings Paper

Adaptive time-frequency decompositions with matching pursuit
Author(s): Geoffrey M. Davis; Stephane G. Mallat; Zhifeng Zhang
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Paper Abstract

To compute the optimal expansion of signals in redundant dictionary of waveforms is an NP complete problem. We introduce a greedy algorithm, called matching pursuit, that performs a suboptimal expansion. The waveforms are chosen iteratively in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions, a matching pursuit defines an adaptive time-frequency transform. We derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit is a chaotic map, whose attractor defines a generic noise with respect to the dictionary. We derive an algorithm that isolates the coherent structures of a signal and an application to pattern extraction from noisy signals is described.

Paper Details

Date Published: 15 March 1994
PDF: 12 pages
Proc. SPIE 2242, Wavelet Applications, (15 March 1994); doi: 10.1117/12.170041
Show Author Affiliations
Geoffrey M. Davis, New York Univ. (United States)
Stephane G. Mallat, New York Univ. (United States)
Zhifeng Zhang, New York Univ. (United States)

Published in SPIE Proceedings Vol. 2242:
Wavelet Applications
Harold H. Szu, Editor(s)

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